Reflected Anime Industry Growth Through the Seasons and Years¶
There were a few things to note:
Overally, yearly anime productions have gone up since the 90s.
Spring has the most releases while Winter has the fewest. However, it is noteworthy that Winter lead in production in 2005 while production of anime in the other seasons dropped.
Fall and Spring numbers have significantly improved over the three decades. Could these seasons seemingly be the most popular among the viewership?
fig = px.line(df_agg, x='year_released', y='title', color='season_released',
labels={'season_released':'Season Released', 'year_released':'Year','title':'Count'}
)
fig.update_layout(title='Growth of Anime Production Through Various Seasons (1992-2018)', template='plotly_white')
fig.add_annotation( # add a text callout with arrow
text="Generally, Spring titles lead the pack 🍃", x="2016", y=160, arrowhead=1, showarrow=True
)
fig.add_annotation(text="Winter production abnormally improved", x="2005", y=65, arrowhead=1, showarrow=True
)
fig.add_shape( # add a horizontal "target" line
type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0=0, x1=1, xref="paper", y0=66, y1=66, yref="y"
)
po.iplot(fig)
Studios Producing the Most Anime Titles (1992-2018)¶
Toei Animation and Sunrise lead the pack by a distance in terms of the titles they have churned out. This could speak to the success they have had in many of their releases: Toei with world renowed One Piece anime, Sunrise with the Cowboy Bebop series, the Gundam franchise to name a few.
Compared to their overall volume in production, Studio Bones, Madhouse, A-1 Pictures, Kyoto Animation (KyoAni ♥) and J.C. Staff have had the most number of top releases in the last 3 decades compared to the rest of the pack. They have garnered the most critical acclaim in the past three decades.
po.init_notebook_mode(connected=True)
fig = px.bar(df_agg,
y='studio', x='count', text='studio', orientation='h',
labels={'studio':'Anime Studios','count':'Number of anime produced'},
title='Top 10 Studios Producing The Most Anime Between 1992-2018'
)
fig.update_yaxes(visible=False, showticklabels=False)
fig.update_layout(template='plotly_dark', yaxis=dict(autorange="reversed"))
po.iplot(fig)
# I tally the appearance of each studio per record as an account of their involvement in the production of the record's title.
# create a dataframe of animes with the top 100 smallest avg_rank
po.init_notebook_mode(connected=True)
fig = px.bar(df_agg,
x='studio', y='count', text='count', orientation='v',
labels={'studio':'Anime Studios','count':'Number of anime produced'},
title='Studios That Produced Animes Titles Rated in the Top 100 (1992-2018)')
fig.update_yaxes(visible=False, showticklabels=False)
fig.update_layout(template='plotly_dark')
po.iplot(fig)
What is the general anime studio preference when it boils down to source material?¶
TV is the most widely used form of anime adaptation, followed by Movie. However, ONAs (Original Net Animations) are still young in the game and low in numbers.
Manga and Original sourced anime are the lead runners in terms of adaptations of titles in the industry. A variety of manga are majorly adapated into TV releases or OVAs (Original Video Animation).
Suprisingly, there have been few manga-movie adaptations in the past three decades. This could be due to the fact that while many popular movies are original creative works intended to convey their thematic messages in a sitting, it's much easier to condense manga into TV adaptations spanning over a period of times.
# aggregating the data by grouping it by the release format and source material
po.init_notebook_mode(connected=True)
fig = px.bar(df_agg,
x='source', y='title', color='type',
title='Anime Adaptations by Release Format and Source Material (1992-2018)',
labels={'type':'Release Format', 'source':'Source Material', 'title': 'Total Adaptations'})
fig.update_layout(barmode='group', yaxis={'categoryorder':'total descending'})
po.iplot(fig)
Shifts in the source material trend over the years (1992-2018)¶
Manga adaptations have been on the rise since the 90s. They are predominantly the most popular source material for anime adaptations.
Original adaptations have also been on the rise seeing as there was a steep increase in their number between 2013 and 2015 consquently toppling Manga adaptations in the latter year. This could be attributed to the critical acclaim and success in 2013 releases such as The Tale of Princess Kaguya by Studio Ghibli, The Garden of Words by renowed director Makoto Shinkai and CoMix Wave Film, 2014 releases such as Evangelion: Final, The Last Naruto Movie, Detective Conan: Dimensional Sniper and 2015 releases such as Tafuu no Norada, Psycho-Pass: The Movie and many others. It's worth noting a number of these titles were original-movie productions.
Suprisingly, there have been few manga-movie adaptations in the past three decades. This could be due to the fact that while many popular movies are original creative works intended to convey their thematic messages in a sitting, it's much easier to condense manga into TV adaptations spanning over a period of times.
Adaptations sourced from Light Novels and Visual Novels have struggled to hit the highs of manga-sourced and original titles.
po.init_notebook_mode(connected=True)
fig = px.line(df_agg,
x='year_released', y='title', color='source',
title='Anime Adaptation Sources (1992-2018)',
labels={'title':'Total adaptations made', 'year_released':'Year Released', 'source':'Source', 'type':'and Release Fomat'}, symbol='type'
)
fig.update_layout(barmode='group', yaxis={'categoryorder':'total descending'})
fig.add_annotation( # add a text callout with arrow
text="Original works have seen a major upshift in the 2010s", x="2016", y=77, arrowhead=1, showarrow=True,
font_size=15
)
po.iplot(fig)
Insights generated from a WordCloud of all reviews text the MAL dataset¶
Words like character, enjoyed, felt, main character, and story gravitate towards strong correlation between Character Development, Enjoyment, and Story.
Music and soundtrack attribute to the importance of the music used in anime productions.
Art and character design are some of the highlights the viewership seeks in Animation.
Due to the frequency of TV content, it's also noteworthy of the word episode being a mainstay point-of-note in the viewership.
Understanding the Relationship Between the Viewership's Polarity and Sentiment Scores¶
Character Development and Enjoyment have a strong correlation with the outcome of the Story score. If an anime does not deliever on the two former features, it's highly likely that it will score quite poorly on the Story factor.
Animation and Music go hand in hand to a certain degree. This explains why certain action scenes in anime could go for fast-paced upbeat tunes while emotive scenes would require songs along the melancholic to poignant scale.
The outstanding sentiment left on a viewer could be highly influenced by how the anime scores in the Character Development, Enjoyment and Story features.
po.init_notebook_mode(connected=True)
fig = px.imshow(df_review_studios[['Story', 'Animation', 'Music', 'Character Development',
'Enjoyment', 'polarity', 'subjectivity']].corr(), color_continuous_scale='icefire',
title='Relationship between Features of Anime as Ranked by Audiences',
text_auto='.2f')
fig.update_layout(width=750, height=700)
po.iplot(fig)
What's the overall attitude and distribution of various sentiments over among the titles released between 1992 - 2018?¶
The outstanding sentiment from the viewership has been a resoundingly Positive one in general. This could be attributed to the "anime boom in the late 90s entering the new millenium".
It's fair to say that the positive outlook on most titles slightly outweighs the volume of anime titles receiving negative criticisms.
po.init_notebook_mode(connected=True)
fig = px.pie(df_reviews, 'attitude', hole=.2)
fig.update_layout(
title_text="Overall Viewer Sentiment of Anime Titles Produced Between 1992 and 2018"
)
po.iplot(fig)
po.init_notebook_mode(connected=True)
values = [len(set(df_anime_reviews[df_anime_reviews.attitude == "Positive"].title)),
len(set(df_anime_reviews[df_anime_reviews.attitude == "Negative"].title)),
len(set(df_anime_reviews[df_anime_reviews.attitude == "Neutral"].title))]
names = ['Postive sentiments', 'Negative sentiments', 'Neutral sentiments']
fig = px.pie(names=names, values=values, title='Percentage Distribution of Titles Receiving Various Sentiments (1992 - 2018)')
fig.update_layout(legend=dict(
orientation="v",
title_text='Titles that received',
yanchor="bottom",
y=0.5,
xanchor="right",
x=0.85
))
po.iplot(fig)
What has been the trend in overall attitude in the anime industry viewership?¶
Generally the anime industry has had a steadily increasing Positive outlook since the turn of the 21st Century.
Neutral outlooks haven't had the most traction and are also outweighed by Negative sentiments.
Positive viewership reviews had the biggest bump in 2006. Notably this was the year that popular releases like Madhouse's Death Note and Black Lagoon, Sunrise's Code Geass: Rebellion of Lelouch and Gintama aired.
Majority of these titles were Action titles, sourced from manga, aired via TV and released in Spring. These titles were either R-rated or PG-13 rated.
po.init_notebook_mode(connected=True)
fig = px.line(df_anime_reviews.groupby(['year_released', 'attitude'])['title'].count().reset_index(),
x='year_released', y='title', color='attitude', color_discrete_sequence=['red', 'blue', 'green'],
labels={'year_released':'Year Released', 'title':'Count', 'attitude':'Attitude'} ,orientation='v',
title='Sentiment Attitude Change Over the Years (1992-2018)')
fig.add_annotation( # add a text callout with arrow
text="<b>Positive viewership reviews had the biggest bump in 2006</b>", x="2006", y=8380, arrowhead=1, showarrow=True,
font_size=15
)
fig.update_layout(template='plotly_white')
po.iplot(fig)
How are viewership ratings affected across various release formats?¶
There is a general preference by the viewership in terms of how Movie releases flesh out the Overall component of each anime title.
Movie and, surprisingly, Music releases tend to have the best-looking Animation according to the viewership. However, the latter scores quite lowly in terms of Character Development due it such videos being 3-5 minute snippets. Personally, I highly reccommend some of Eve's or n-buna/Yorushika's works.
OVAs are the least favoured kind of releases based on the viewership's preferences.
po.init_notebook_mode(connected=True)
df_review_type = df_anime_reviews.groupby(['type'])[['Overall', 'Story', 'Animation', 'Music',
'Character Development', 'Enjoyment']].mean()
fig = px.bar(df_review_type.sort_values('Overall',ascending=False), text='value', text_auto='.1f',
title='Feature Scores across Various Anime Release Formats',
labels={'value':'Score', 'type':'Release Format', 'variable':'Features'})
fig.update_layout(barmode='stack')
po.iplot(fig)
What is the link between the seasons and ratings through the years?¶
Undisputedly, Fall anime have been the most succesful releases among the anime community the past in 20 years. This speaks volumes as to why certain fan-favorite reboots and long-awaited releases like Bleach, Chainsaw Man, Jujutsu Kaisen among others are set for release in the Fall period.
This could also attribute to changes in the 2-cour system where anime is released in two parts through seperate seasons. There could be a possible emergence of anime being released through Summer into the Fall causing a boost in Summer releases. One anime that could be alluded to using this format in 2022 is Spy x Family.
Fall and Spring releases have seen high volumes of production in the 2010s. This could be attributed to a number of factors like technological advancement, boom in the anime movie franchise,
The PG-13 rating has been highly pre-dominant in terms of volume of titles since the 90s through various seasons.
Meanwhile, more senstive anime with adult themes (R-rated content) picked up from 2004 gradually increasing in volume from 2008 with a majority of them released in the Winter period.
po.init_notebook_mode(connected=False)
fig = px.scatter(df_agg.head(200),
x='year_released', y='title', color='season_released', labels={'title':'Number of anime releases',
'year_released':'Year of Release', 'season_released':'Season'},
title="Seasonal Releases and Ratings Through the 90s to 2018",
color_discrete_sequence=["magenta", "green", "blue", "goldenrod"],
symbol='rating', size=(df_agg['title'].head(200))/10, animation_frame='season_released',
symbol_sequence= ['circle-open-dot', 'square-open-dot', 'octagon-open-dot', 'cross-open-dot', 'x-open-dot',
'cross-open-dot', 'hexagram-open-dot'])
fig.update_layout(template='plotly_dark')
po.iplot(fig)
Key Observation¶
Studio Gainax's and Tatsunoko Productions's work is highly rated on average amongst the anime community with this being world-renowed Neon Genesis Evangelion. NO VIZ
This was an original TV anime released in the Fall. It's main genre was Action and had a rating of PG-13. NO VIZ
